A Multi-Armed Bandit Model Selection for Cold-Start User Recommendation

@article{Felcio2017AMB,
  title={A Multi-Armed Bandit Model Selection for Cold-Start User Recommendation},
  author={Cr{\'i}cia Z. Fel{\'i}cio and Kl{\'e}risson Vin{\'i}cius Ribeiro Paix{\~a}o and C{\'e}lia A. Zorzo Barcelos and Philippe Preux},
  journal={Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization},
  year={2017}
}
How can we effectively recommend items to a user about whom we have no information? This is the problem we focus on in this paper, known as the cold-start problem. In most existing works, the cold-start problem is handled through the use of many kinds of information available about the user. However, what happens if we do not have any information? Recommender systems usually keep a substantial amount of prediction models that are available for analysis. Moreover, recommendations to new users… 

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